In computerized tomography, it is important to reduce the image noise without increasing the acquisition dose.
Extensive research has been done into total variation minimization for image denoising and sparse-view reconstruction.
However, TV minimization methods show superior denoising performance for simple images (with
little texture), but result in texture information loss when applied to more complex images. Since in medical
imaging, we are often confronted with textured images, it might not be beneficial to use TV. Our objective is to
find a regularization term outperforming TV for sparse-view reconstruction and image denoising in general. A
recent efficient solver was developed for convex problems, based on a split-Bregman approach, able to incorporate
regularization terms different from TV. In this work, a proof-of-concept study demonstrates the usage of the
discrete shearlet transform as a sparsifying transform within this solver for CT reconstructions. In particular,
the regularization term is the 1-norm of the shearlet coefficients. We compared our newly developed shearlet
approach to traditional TV on both sparse-view and on low-count simulated and measured preclinical data.
Shearlet-based regularization does not outperform TV-based regularization for all datasets. Reconstructed images
exhibit small aliasing artifacts in sparse-view reconstruction problems, but show no staircasing effect. This
results in a slightly higher resolution than with TV-based regularization.
In emission tomography, iterative reconstruction is usually followed by a linear smoothing filter to make such images more appropriate for visual inspection and diagnosis by a physician. This will result in a global blurring of the images, smoothing across edges and possibly discarding valuable image information for detection tasks. The purpose of this study is to investigate which possible advantages a non-linear, edge-preserving postfilter could have on lesion detection in Ga-67 SPECT imaging. Image quality can be defined based on the task that has to be performed on the image. This study used LROC observer studies based on a dataset created by CPU-intensive Gate Monte Carlo simulations of a voxelized digital phantom. The filters considered in this study were a linear Gaussian filter, a bilateral filter, the Perona-Malik anisotropic diffusion filter and the Catte filtering scheme.
The 3D MCAT software phantom was used to simulate the distribution of Ga-67 citrate in the abdomen. Tumor-present cases had a 1-cm diameter tumor randomly placed near the edges of the anatomical boundaries of the kidneys, bone, liver and spleen. Our data set was generated out of a single noisy background simulation using the bootstrap method, to significantly reduce the simulation time and to allow for a larger observer data set. Lesions were simulated separately and added to the background afterwards. These were then reconstructed with an iterative approach, using a sufficiently large number of MLEM iterations to establish convergence.
The output of a numerical observer was used in a simplex optimization method to estimate an optimal set of parameters for each postfilter.
No significant improvement was found for using edge-preserving filtering techniques over standard linear Gaussian filtering.
The main goal of this work is to assess the overall imaging performance of dedicated new solid state devices compared to a traditional scintillation camera for use in SPECT imaging. A solid state detector with a rotating slat collimator will be compared with the same detector mounted with a classical collimator as opposed to a traditional Anger camera. A better energy resolution characterizes the solid state materials while the rotating slat collimator promises a better sensitivity-resolution tradeoff. The evaluation of the different imaging modalities is done using GATE, a recently developed Monte Carlo code. Several features for imaging performance evaluation were addressed: spatial resolution, energy resolution, sensitivity, and a ROC analysis was performed to evaluate the hot spot detectability. In this way a difference in perfromance was concluded for the diverse imaging techniques which allows a task dependent application of these modalities in future clinical practice.
In this paper, we will describe a theoretical model of the spatial uncertainty for a line of response, due to the imperfect localization of events on the detector heads of the Positron Emission Tomography (PET) camera. We assume a Gaussian distribution of the position of interaction on a detector head, centered at the measured position. The probability that an event originates from a certain point in the FOV is calculated by integrating all the possible LORs through this point, weighted with the Gaussian probability of detection at the LORs end points. We have calculated these probabilities both for perpendicular and oblique coincidences. For the oblique coincidence case it was necessary to incorporate the effect of the crystal thickness in the calculations. We found that the probability function can not be analytically expressed in a closed form, and it was thus calculated by means of numerical integration. A Gaussian was fitted to the probability profiles for a given distance to the detectors. From these fits, we can conclude that the profiles can be accurately approximated by a Gaussian, both for perpendicular as for oblique coincidences. The FWHM reaches a maximum at the detector heads, and decreases towards the center of the FOV, as was expected.
The accurate quantification of brain perfusion for emission computed tomography data (PET-SPECT) is limited by partial volume effects (PVE). This study presents a new approach to estimate accurately the true tissue tracer activity within the grey matter tissue compartment. The methodology is based on the availability of additional anatomical side information and on the assumption that activity concentration within the white matter tissue compartment is constant. Starting from an initial estimate for the white matter grey matter activity, the true tracer activity within the grey matter tissue compartment is estimated by an alternating ML-EM-algorithm. During the updating step the constant activity concentration within the white matter compartment is modelled in the forward projection in order to reconstruct the true activity distribution within the grey matter tissue compartment, hence reducing partial volume averaging. Consequently the estimate for the constant activity in the white matter tissue compartment is updated based on the new estimated activity distribution in the grey matter tissue compartment. We have tested this methodology by means of computer simulations. A T1-weighted MR brainscan of a patient was segmented into white matter, grey matter and cerebrospinal fluid, using the segmentation package of the SPM-software (Statistical Parametric Mapping). The segmented grey and white matter were used to simulate a SPECT acquisition, modelling the noise and the distance dependant detector response. Scatter and attenuation were ignored. Following the above described strategy, simulations have shown it is possible to reconstruct the true activity distribution for the grey matter tissue compartment (activity/tissue volume), assuming constant activity in the white matter tissue compartment.